Package: theftdlc 0.2.2

Trent Henderson

theftdlc: Analyse and Interpret Time Series Features

Provides a suite of functions for analysing, interpreting, and visualising time-series features calculated from different feature sets from the 'theft' package. Implements statistical learning methodologies described in Henderson, T., Bryant, A., and Fulcher, B. (2023) <doi:10.48550/arXiv.2303.17809>.

Authors:Trent Henderson [cre, aut]

theftdlc_0.2.2.tar.gz
theftdlc_0.2.2.zip(r-4.7)theftdlc_0.2.2.zip(r-4.6)theftdlc_0.2.2.zip(r-4.5)
theftdlc_0.2.2.tgz(r-4.6-any)theftdlc_0.2.2.tgz(r-4.5-any)
theftdlc_0.2.2.tar.gz(r-4.7-any)theftdlc_0.2.2.tar.gz(r-4.6-any)
theftdlc_0.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
theftdlc/json (API)

# Install 'theftdlc' in R:

install.packages('theftdlc', repos = c('https://hendersontrent.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hendersontrent/theftdlc/issues

Pkgdown/docs site:https://hendersontrent.github.io

On CRAN:

data-science data-visualization machine-learning statistics time-series

5.33 score 5 stars 17 scripts 193 downloads 10 exports 110 dependencies

Last updated from:22c91ee652. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	252
source / vignettes	OK	287
linux-release-x86_64	OK	267
macos-release-arm64	OK	144
macos-oldrel-arm64	OK	188
windows-devel	OK	212
windows-release	OK	258
windows-oldrel	OK	171
wasm-release	OK	153

Exports:calculate_interval classify cluster compare_features filter_duplicates interval project reduce_dims shrink tsfeature_classifier

Dependencies:anytime askpass backports BH broom class cli codetools colorspace correctR cpp11 curl digest distributional dplyr e1071 fabletools farver feasts foreach forecast fracdiff furrr future generics ggdist ggplot2 ggtime glmnet globals glue gtable here hms isoband iterators janitor jsonlite labeling lattice lifecycle listenv lmtest lubridate magrittr MASS Matrix mclust nlme nnet normaliseR numDeriv openssl parallelly pillar pkgconfig plyr png progressr proxy purrr quadprog quantmod R.matlab R.methodsS3 R.oo R.utils R6 rappdirs Rcatch22 RColorBrewer Rcpp RcppArmadillo RcppEigen RcppRoll RcppTOML reshape2 reticulate rlang rprojroot RSpectra Rtsne S7 scales shape slider snakecase stringi stringr survival sys theft tibble tidyr tidyselect timechange timeDate tseries tsfeatures tsibble TTR umap urca utf8 vctrs viridisLite warp withr xts zoo

Introduction to theftdlc

Rendered fromtheftdlc.Rmdusingknitr::rmarkdown

Last update: 2025-12-24
Started: 2024-02-25

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Fit classifiers using time-series features using a resample-based approach and get a fast understanding of performance	classify tsfeature_classifier
Perform cluster analysis of time series using their feature vectors	cluster
Conduct statistical testing on time-series feature classification performance to identify top features or compare entire sets	compare_features
Remove duplicate features that exist in multiple feature sets and retain a reproducible random selection of one of them	filter_duplicates
Filter resample data sets according to good feature list	filter_good_features
Helper function to find features in both train and test set that are "good"	find_good_features
Fit classification model and compute key metrics	fit_models
Calculate central tendency and spread values for all numeric columns in a dataset	get_rescale_vals
Calculate interval summaries with a measure of central tendency of classification results	calculate_interval interval
Helper function for converting to title case	make_title
Produce a plot for a feature_calculations object	plot.feature_calculations
Produce a plot for a feature_projection object	plot.feature_projection
Produce a plot for a interval_calculations object	plot.interval_calculations
Project a feature matrix into a two-dimensional representation using PCA, MDS, t-SNE, or UMAP ready for plotting	project reduce_dims
Helper function to create a resampled dataset	resample_data
Calculate z-score for all columns in a dataset using train set central tendency and spread	rescale_zscore
Helper function to select only the relevant columns for statistical testing	select_stat_cols
Use a cross validated penalized maximum likelihood generalized linear model to perform feature selection	shrink
Calculate p-values for feature sets or features relative to an empirical null or each other using resampled t-tests	stat_test
Analyse and Interpret Time Series Features	theftdlc